A Fully Time-domain Neural Model for Subband-based Speech Synthesizer
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This paper introduces a deep neural network model for subband-based speech synthesizer. The model benefits from the short bandwidth of the subband signals to reduce the complexity of the time-domain speech generator. We employed the multi-level wavelet analysis/synthesis to decompose/reconstruct the signal to subbands in time domain. Inspired from the WaveNet, a convolutional neural network (CNN) model predicts subband speech signals fully in time domain. Due to the short bandwidth of the subbands, a simple network architecture is enough to train the simple patterns of the subbands accurately. In the ground truth experiments with teacher forcing, the subband synthesizer outperforms the fullband model significantly. In addition, by conditioning the model on the phoneme sequence using a pronunciation dictionary, we have achieved the first fully time-domain neural text-to-speech (TTS) system. The generated speech of the subband TTS shows comparable quality as the fullband one with a slighter network architecture for each subband.
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